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Linear problems for univariate functions with noisy data

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Part of the Lecture Notes in Mathematics book series (LNM,volume 1733)

Keywords

  • Univariate Function
  • Linear Problem
  • Minimal Error
  • Noisy Data
  • Smoothing Spline

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2.1. Notes and References

3.1. Notes and References

  • Ritter, K. (1996b), Almost optimal differentiation using noisy data, J. Approx. Theory bf 86, 293–309.

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  • Micchelli, C. A. (1976), On an optimal method for the numerical differentiation of smooth functions, J. Approx. Theory 18, 189–204.

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  • Kwong, Man Kam, and Zettl, A. (1992), Norm inequalities for derivatives and differences, Lect. Notes in Math. 1536, Springer-Verlag, Berlin.

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  • Donoho, D. L. (1994), Asymptotic minimax risk for sup-norm loss: solution via optimal recovery, Probab. Theory Relat. Fields 99, 145–170.

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© 2000 Springer-Verlag

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Ritter, K. (2000). Linear problems for univariate functions with noisy data. In: Ritter, K. (eds) Average-Case Analysis of Numerical Problems. Lecture Notes in Mathematics, vol 1733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0103939

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  • DOI: https://doi.org/10.1007/BFb0103939

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67449-8

  • Online ISBN: 978-3-540-45592-9

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